import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import textwrap
import calendar

df = pd.read_csv("us_tornado_dataset_1950_2021.csv")
df

	yr	mo	dy	date	st	mag	inj	fat	slat	slon	elat	elon	len	wid
0	1950	1	3	1950-01-03	IL	3	3	0	39.1000	-89.3000	39.1200	-89.2300	3.60	130
1	1950	1	3	1950-01-03	MO	3	3	0	38.7700	-90.2200	38.8300	-90.0300	9.50	150
2	1950	1	3	1950-01-03	OH	1	1	0	40.8800	-84.5800	0.0000	0.0000	0.10	10
3	1950	1	13	1950-01-13	AR	3	1	1	34.4000	-94.3700	0.0000	0.0000	0.60	17
4	1950	1	25	1950-01-25	IL	2	0	0	41.1700	-87.3300	0.0000	0.0000	0.10	100
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
67553	2021	12	30	2021-12-30	GA	1	0	0	31.1703	-83.3804	31.1805	-83.3453	2.19	150
67554	2021	12	30	2021-12-30	GA	1	0	0	31.6900	-82.7300	31.7439	-82.5412	11.71	300
67555	2021	12	31	2021-12-31	AL	1	0	0	34.2875	-85.7878	34.2998	-85.7805	0.95	50
67556	2021	12	31	2021-12-31	GA	1	0	0	33.7372	-84.9998	33.7625	-84.9633	2.75	150
67557	2021	12	31	2021-12-31	GA	1	6	0	33.5676	-83.9877	33.5842	-83.9498	2.50	75

67558 rows × 14 columns

df.rename(columns = {'fat':'fatalities', 'len':'length', 'st':'state','inj':'injuries'}, inplace = True)
df

	yr	mo	dy	date	state	mag	injuries	fatalities	slat	slon	elat	elon	length	wid
0	1950	1	3	1950-01-03	IL	3	3	0	39.1000	-89.3000	39.1200	-89.2300	3.60	130
1	1950	1	3	1950-01-03	MO	3	3	0	38.7700	-90.2200	38.8300	-90.0300	9.50	150
2	1950	1	3	1950-01-03	OH	1	1	0	40.8800	-84.5800	0.0000	0.0000	0.10	10
3	1950	1	13	1950-01-13	AR	3	1	1	34.4000	-94.3700	0.0000	0.0000	0.60	17
4	1950	1	25	1950-01-25	IL	2	0	0	41.1700	-87.3300	0.0000	0.0000	0.10	100
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
67553	2021	12	30	2021-12-30	GA	1	0	0	31.1703	-83.3804	31.1805	-83.3453	2.19	150
67554	2021	12	30	2021-12-30	GA	1	0	0	31.6900	-82.7300	31.7439	-82.5412	11.71	300
67555	2021	12	31	2021-12-31	AL	1	0	0	34.2875	-85.7878	34.2998	-85.7805	0.95	50
67556	2021	12	31	2021-12-31	GA	1	0	0	33.7372	-84.9998	33.7625	-84.9633	2.75	150
67557	2021	12	31	2021-12-31	GA	1	6	0	33.5676	-83.9877	33.5842	-83.9498	2.50	75

67558 rows × 14 columns

df.isna().sum()

yr            0
mo            0
dy            0
date          0
state         0
mag           0
injuries      0
fatalities    0
slat          0
slon          0
elat          0
elon          0
length        0
wid           0
dtype: int64

df['mag'].unique()

array([ 3,  1,  2,  4,  0,  5, -9], dtype=int64)

df['mag'].value_counts()

 0    31375
 1    22885
 2     9517
 3     2536
-9      605
 4      581
 5       59
Name: mag, dtype: int64

df1 = df.groupby('yr')['yr'].count()


plt.plot(df1)
plt.xlabel('Year')
plt.ylabel('Number of Tornadoes')
plt.title('Tornadoes in the US',fontweight='bold')

Text(0.5, 1.0, 'Tornadoes in the US')

df2 = df.groupby('state')['state'].count().reset_index(name = 'counts').sort_values(['counts'],ascending = False)
df2.head(10).plot(x = 'state', kind = 'bar')
plt.xlabel('State')
plt.ylabel('Number of tornadoes')
plt.title('Top 10 states with the highest number of tornadoes',fontweight='bold')

Text(0.5, 1.0, 'Top 10 states with the highest number of tornadoes')

import plotly.express as px

fig = px.choropleth(df2,
                     locations = 'state',
                     locationmode = 'USA-states',
                     color = 'counts',
                     color_continuous_scale = 'REDS',
                     scope = 'usa',
                    
                   )

fig.update_layout(title_text='Count of tornadoes across the states in the US.<br>(Hover for statewise counts)')

fig.show()

df_months_count = df.groupby('mo')['mo'].count().reset_index(name = 'monthly_tornadoes')
df_months_count['mo'] = df_months_count['mo'].apply(lambda x: calendar.month_abbr[x])
df_months_count.plot(x = 'mo', kind = 'bar')
plt.xlabel('Months')
plt.ylabel('Number of tornadoes')
plt.title('Number of tornadoes per month throughout the US',fontweight='bold')

Text(0.5, 1.0, 'Number of tornadoes per month throughout the US')

fatalities = df.groupby('state')['fatalities'].sum().reset_index().sort_values('fatalities', ascending = False)
fatalities.head(10).plot(x = 'state', kind = 'bar')

<AxesSubplot:xlabel='state'>